Measuring degrees of populism in party manifestos using supervised machine learning

[chengjun]

Di Cocco, Jessica, and Bernardo Monechi. "How populist are parties? Measuring degrees of populism in party manifestos using supervised machine learning." Political Analysis 30.3 (2022): 311-327. DOI: https://doi.org/10.1017/pan.2021.29

How Populist are Parties? Measuring Degrees of Populism in Party Manifestos Using Supervised Machine Learning

如何衡量西方政党的民粹程度？一个监督机器学习方法 | Political Analysis

汪浩東 PoIiticaI理论志

https://mp.weixin.qq.com/s/BdPHheUEv5DOufXgp7162w

编者荐语：
这篇研究展示了机器学习在衡量西方民粹主义程度上的潜力。本文通过使用基于监督机器学习的文本即数据方法，提出了一种衡量政党民粹主义水平的系统方法，作者通过对欧洲六国近 20 年268 个政党的选举宣言中提取的 243659 条句子进行了文本分析，揭示了民粹主义在各国的演变趋势。实验结果表明，各国民粹主义程度间存在显着差异。民粹主义在意大利急剧上升，而在其他国家则呈现出不均衡的趋势。这篇文章介绍的方法对民粹主义方法论提供了重要的启发，该方法还可以应用于检查更大的时间间隔、其他类型的文本来源或其他类型的政治和社会现象的演变与发展。

Published online by Cambridge University Press: 15 October 2021

Jessica Di Cocco and Bernardo Monechi

Abstract

One of the main challenges in comparative studies on populism concerns its temporal and spatial measurements within and between a large number of parties and countries. Textual analysis has proved useful for these purposes, and automated methods can further improve research in this direction. Here, we propose a method to derive a score of parties’ levels of populism using supervised machine learning to perform textual analysis on national manifestos. We illustrate the advantages of our approach, which allows for measuring populism for a vast number of parties and countries without resource-intensive human-coding processes and provides accurate, updated information for temporal and spatial comparisons of populism. Furthermore, our method allows for obtaining a continuous score of populism, which ensures more fine-grained analyses of the party landscape while reducing the risk of arbitrary classifications. To illustrate the potential contribution of this score, we use it as a proxy for parties’ levels of populism, analyzing average trends in six European countries from the early 2000s for nearly two decades.

Replication code

for this article is available at https://doi.org/10.7910/DVN/BMJYAN (Di Cocco and Monechi Reference Di Cocco and Monechi2021).

or you can access the scripts and data from github

https://github.com/bernomone/howPopParties_addendum